Cell-based assays have become indispensable tools in drug discovery and development and biological investigations. Cell-based assays are used for monitoring cell health and cell death under various conditions. For example, cell-based assays allow quantitation of cell viability and cell proliferation. Cell-based assays are also used for monitoring molecular processes in cells, such as activation of particular signaling pathways, receptor binding, ion concentrations, membrane potential, specific translocations, enzyme activities, gene expression, as well as the presence, amounts and patterns of cellular molecules, e.g., metabolites, proteins, lipids, carbohydrates, and nucleic acid sequences. Thus, cell-based assays allow investigation of molecular mechanisms of diseases and drug effects. Cell-based assays can be performed with either living cells or fixed-cell preparations, and either on a single cell basis or on a cell population basis. In drug development, cell-based assays are now used in almost all phases from primary screening to in vitro toxicity evaluation.
Cell-based assays are commensurate with high-throughput screen (HTS) and high-content screen (HCS). This is especially important in drug discovery. High-throughput screens are often carried out using a parallel assay format in which multiple samples are screened concurrently. For example, high throughput screens of a large number of different chemical compounds and/or biological agents are often carried out using arrays of wells, e.g., in standard microtiter plates with 96, 384 or 1536 wells. The signal measured from each well, e.g., fluorescence emission or optical density, integrates the signal from all the material in the well to give an overall population average of all the molecules in the well. Large scale cell-based screens of interactions between drugs and an siRNA library was disclosed in U.S. Patent Application Publication No. 2005-0181385, published on Aug. 18, 2005.
High-content screens allow monitoring multiple molecules and/or processes. For example, high-content screens can be performed with multiple fluorescence labels of different colors (Giuliano et al., 1995, Curr. Op. Cell Biol. 7:4; Giuliano et al., 1995, Ann. Rev. Biophys. Biomol. Struct. 24:405). In a high-content screen, both spatial and temporal dynamics of various cellular processes can be monitored (Farkas et al., 1993, Ann. Rev. Physiol. 55:785; Giuliano et al., 1990, In Optical Microscopy for Biology. B. Herman and K. Jacobson (eds.), pp. 543-557, Wiley-Liss, New York; Hahn et al., 1992, Nature 359:736; Waggoner et al., 1996, Hum. Pathol. 27:494). Single cell measurements can also be performed. Each cell can be treated as a “well” that has spatial and temporal information on the activities of the labeled constituents.
In addition to microtiter plate and flow cytometry, cell-based assays can also be performed using cell microarrays (Ziauddin et al., Nature 411:107-110; Bailey et al., DDT 7, No. 18 (supplement): 1-6). Cell microarrays can be generated by printing cDNA-containing plasmids on a surface. The printed arrays are then exposed to a lipid transfection reagent to form lipid-DNA complexes on the surface. Cells are then added to the surface. Clusters of cells transfected by cDNA contained in a plasmid printed on the surface are generated at the location of the printed plasmid. Such cell microarrays can contain as high as 6,000 to 10,000 spots per slide. Each spot contains a cluster of about 100 transfected cells.
High-throughput DNA array technologies have made it possible to monitor the expression level of a large number of genetic transcripts at any one time (see, e.g., Schena et al., 1995, Science 270:467-470; Lockhart et al., 1996, Nature Biotechnology 14:1675-1680; Blanchard et al., 1996, Nature Biotechnology 14:1649; Ashby et al., U.S. Pat. No. 5,569,588, issued Oct. 29, 1996). By simultaneously monitoring tens of thousands of genes, DNA array technologies have allowed, inter alia, genome-wide analysis of mRNA expression in a cell or a cell type or any biological sample. Aided by sophisticated data management and analysis methodologies, the transcriptional state of a cell or cell type as well as changes of the transcriptional state in response to external perturbations, including but not limited to drug perturbations, can be characterized on the mRNA level (see, e.g., Stoughton et al., International Publication No. WO 00/39336, published Jul. 6, 2000; Friend et al., International Publication No. WO 00/24936, published May 4, 2000). Applications of such technologies include, for example, identification of genes which are up regulated or down regulated in various physiological states, particularly diseased states. Additional exemplary uses for DNA arrays include the analyses of members of signaling pathways, and the identification of targets for various drugs. See, e.g., Friend and Hartwell, International Publication No. WO 98/38329 (published Sep. 3, 1998); Stoughton, International Publication No. WO 99/66067 (published Dec. 23, 1999); Stoughton and Friend, International Publication No. WO 99/58708 (published Nov. 18, 1999); Friend and Stoughton, International Publication No. WO 99/59037 (published Nov. 18, 1999); Friend et al., U.S. Pat. No. 6,218,122.
Protein microarrays are used to monitor the genome-wide protein expression in cells (i.e., the “proteome,” Goffeau et al., 1996, Science 274:546-567; Aebersold et al., 1999, Nature Biotechnology 10:994-999). Protein microarrays contain binding sites comprise immobilized, preferably monoclonal, antibodies specific to a plurality of protein species encoded by the cell genome (see, e.g., Zhu et al., 2001, Science 293:2101-2105; MacBeath et al., 2000, Science 289:1760-63; de Wildt et al., 2000, Nature Biotechnology 18:989-994). Protein expression in a cell can also be separated and measured by two-dimensional gel electrophoresis techniques. Two-dimensional gel electrophoresis is well-known in the art and typically involves iso-electric focusing along a first dimension followed by SDS-PAGE electrophoresis along a second dimension. See, e.g., Hames et al., 1990, Gel Electrophoresis of Proteins: A Practical Approach, IRL Press, New York; Shevchenko et al., 1996, Proc. Natl. Acad. Sci. USA 93:1440-1445; Sagliocco et al., 1996, Yeast 12:1519-1533; Lander, 1996, Science 274:536-539; and Beaumont et al., Life Science News 7, 2001, Amersham Pharmacia Biotech. The resulting electropherograms can be analyzed by numerous techniques, including mass spectrometric techniques, Western blotting and immunoblot analysis using polyclonal and monoclonal antibodies, and internal and N-terminal micro-sequencing. Using these techniques, it is possible to identify a substantial fraction of all the proteins produced under given physiological conditions, including in cells (e.g., in yeast) exposed to a drug, or in cells modified by, e.g., deletion or over-expression of a specific gene.
In these screens, it is often desirable to compare a measurement of a variable in a sample of interest with that in a reference sample to determine the change in the measurement relative to the reference sample (see, e.g., U.S. Patent Application Publication No. 2005-0181385, published on Aug. 18, 2005). For example, in a cell-based drug screen assay, it is often desirable to determine the difference in growth rate of cells under the treatment of a drug versus that of cells not under the treatment. Thus, in such screens, measurements of one or more reference samples are often made concurrently with the treated sample. A metric of the difference between the measurement of the test sample and the reference sample is used as a measure of the change. The measured changes under different conditions are then compared. In order to reliably compare changes, the errors in the measured changes are needed. Because the reference contains errors, the error of the metric must include reference errors. However, due to low number of replicate reference measurements, error estimation for the reference measurements using the traditional approach is often not accurate. There is therefore a need for a more accurate method of estimating reference errors.
High content, high throughput and miniature assays are most easily achieved using fluorescence detection. For example, fluorescence dye-based assays for cell viability and cytotoxicity are reliable and easy to perform. Multiple samples may be monitored concurrently. Fluorescence-based assays require low volumes of reagents and test compounds. Fluorescence-based assays permit monitoring multiple variables by using fluorescence labels of different emission wavelengths. For example, simultaneous two-color measurements of numbers of live and dead cells permit assaying the viability status of mixed-cell populations.
The measured fluorescence intensity for each probe site, be it a single cell or a population of cells, comes from various sources, e.g., signal from the intended species, noise due to background, etc. The average intensity within a probe site can be measured by the median image value on the site. This intensity serves as a measure of the total photons emitted from the sample for the measured wavelength. The median is used as the average to mitigate the effect of outlying pixel values created by noise. See, e.g., U.S. Patent Application Publication No. 2003-0226098, published on Dec. 4, 2003
Measurement error in a measured signal comes from various sources, including those that fall into the following three categories: additive error, multiplicative error, and Poisson error. The signal magnitude-independent or intensity-independent additive error includes errors resulted from, e.g., background fluctuation, or site-to-site variations (e.g., well-to-well variations in microtiter plate experiment and spot-to-spot variations in a microarray experiment) in signal intensity among negative control sites, etc. The signal magnitude-dependent or intensity-dependent multiplicative error, which is proportional to the signal intensity, includes errors resulted from, e.g., the scatter observed for ratios that should be unity. The multiplicative error is also termed fractional error. The third type of error is a result of variation in number of available binding sites in a spot. This type of error depends on the square-root of the signal magnitude, e.g., measured intensity. It is also called the Poisson error, because it is believed that the number of binding sites on a microarray spot follows a Poisson distribution, and has a variance which is proportional to the average number of binding sites. Errors in measured data can be described by error models (see, e.g., Supplementary material to Roberts et al., 2000, Science, 287:873-880; U.S. Patent Application Publication No. 2003-0226098, published on Dec. 4, 2003; and Rocke et al., 2001, J. Computational Biology 8:557-569). U.S. Patent Application Publication No. 2003-0226098, published on Dec. 4, 2003, discloses methods for analyzing measurement errors in measured signals obtained in an experiment, e.g., measured intensity signals obtained in a microarray gene expression experiment. The application discloses a method for transforming measured signals into a domain in which the measurement errors in the transformed signals are normalized by errors as determined from an error model. The methods are particularly useful for analyzing measurement errors in signals in which at least portion of the error is dependent on the magnitudes of the signals. Such transformed signals permit analysis of data using traditional statistical methods, e.g., ANOVA and regression analysis. Magnitude-independent errors can also be used for comparing level of measurement errors in signals of different magnitudes.
U.S. Pat. No. 6,691,042 discloses methods for generating differential profiles A vs. B, i.e., differential profiles between samples having been subject to condition A and condition B, from data obtained in separately performed experimental measurements A vs. C and B vs. D. When C and D are the same, i.e., common, the methods involve determination of systematic measurement errors or biases between measurements carried out in different experimental reactions, i.e., cross-experiment errors or biases, using data measured for samples under the common condition and for removal or reduction of such cross-experiment errors. U.S. Pat. No. 6,691,042 also provides methods for generating differential profiles A vs. B from data obtained in separately performed single-channel measurements A and B.
U.S. Patent Application Publication No. 2004-0143399, published on Jul. 22, 2004, discloses improved ANOVA methods for analyzing measured data and transformed data. The improved ANOVA method takes two data types as its input, the measurements and a predetermined error associated with the measurements. The latter can come from a technology/platform-specific error model. Because of the additional input information, the statistical power is increased.
Discussion or citation of a reference herein shall not be construed as an admission that such reference is prior art to the present invention.